Predictive Analytics for Mortality: FSRNCA-FLANN Modeling Using Public Health Inventory Records

Predictive analytics involves the use of Artificial Intelligence (AI) and Machine Learning (ML) techniques to analyze current and historical data, identify patterns, and make predictions about future outcomes. In the context of healthcare, predictive analytics is invaluable for understanding and addressing various challenges, with a particular emphasis on predicting mortality rates in specific communities during illnesses such as diabetes, heart disease, and drug overdose. In this study, we propose a unique approach combining Feature Selection Regression based on Neighborhood Component Analysis (FSRNCA) with a less complex single-layer Functional Link Artificial Neural Network (FLANN) model to predict mortality rates using publicly available open-source inventory, i.e., Big Cities Health Inventory (BCHI). Our methodology leverages regularized FSRNCA to select relevant features from the BCHI dataset, considering factors such as demographics, socio-economic indicators, and health metrics. Subsequently, the FLANN model is trained using the selected features to predict mortality rates in diverse urban populations. The model achieves a high R2 score of 91%, outperforming other competitive ML algorithms. Additionally, the proposed technique is successfully tested with datasets such as PIMA, BUPA, ECOLI, and LYMPHOGRAPHY for the classification of various other illnesses. Furthermore, FLANN exhibits minimal computational complexity and rapid training-testing times, highlighting its practicality for real-world applications. The minimal computational complexity and training-testing time between the dense multilayer neural networks reveal the excellent potential and utility of the proposed technique.